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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Federated Learning for Medical Image Analysis with Deep Neural Networks.

Sajid Nazir¹, Mohammad Kaleem²

¹Department of Computing, Glasgow Caledonian University, Glasgow G4 0BA, UK.

Diagnostics (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

Federated learning (FL) enhances medical image analysis using deep neural networks (DNN) by training models locally, preserving data privacy. This approach addresses security concerns, enabling wider application of DNNs in clinical diagnosis.

Keywords:

blockchain cryptography data privacy deep neural networks disease diagnosis model generalization

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Science

Background:

Deep neural networks (DNNs) excel in medical image analysis for disease diagnosis.
Data quality and quantity are crucial for DNN accuracy.
Sharing medical data for training is restricted by privacy and security concerns.

Purpose of the Study:

To review federated learning (FL) applications in medical image analysis using DNNs.
To highlight security concerns associated with FL in this domain.
To explore efforts in improving FL performance and identify future research directions.

Main Methods:

Review of existing literature on federated learning in medical image analysis.
Analysis of security implications and privacy-preserving techniques.
Examination of methods to enhance federated learning model performance.

Main Results:

Federated learning enables collaborative DNN training on decentralized medical data.
FL mitigates privacy risks inherent in centralized data sharing.
Various strategies exist to improve FL model accuracy and efficiency.

Conclusions:

Federated learning is a promising approach for secure and private medical image analysis.
Addressing security challenges and improving FL performance are key for clinical adoption.
Further research is needed to fully realize the potential of FL in healthcare.